Emitter identification from radio signals using keyclick algorithm

ABSTRACT

An algorithm for emitter identification of a transmitting platform using microphone keyclick analysis, and a system utilizing said algorithm, wherein said algorithm processes audio signals to detect transmission activity on a radio channel in the audio frequency range, detects the boundaries of transmitter keyclicks at the beginning and end of said transmission; extracts keying envelopes from said detected keyclick boundaries; and analyzes said keying envelopes to extract features characteristic of said keyclicks. The algorithm and system then proceed to execute a decision function based on said extracted characteristic features to identify the platform of said transmitter based on the features of said keyclick. A second algorithm, which may be executed in parallel with the first by said system, processes said transmission data using a fast fourier transform, detecting gaps in said processed transmission data. It then extracts spectral features consisting of normalized energy per unit frequency from said gaps; and correlates identification of said emitter platforms based on said extracted spectral features with identification of said emitter platforms based on said extracted keyclick features.

CROSS-REFERENCE TO RELATED APPLICATION

This application is related to another application entitled EMITTERIDENTIFICATION FROM RADIO SIGNALS USING FILTER BANK ALGORITHM, filedsimultaneously herewith, having the same inventors and assigned to thesame assignee as this application, and which is incorporated herein byreference.

This invention was made with Government support under Contract No.F30602-87-D-0085/0010 awarded by the Department of the Air Force. TheGovernment has certain rights in this invention.

BACKGROUND OF THE INVENTION

This application pertains generally to digital signal processing, and inparticular to the processing of detected radio signals emitted in theaudio frequency bands. Specifically, this invention pertains to analgorithm which analyzes unintentional emissions from audio frequencyplatforms using push-to-talk transmitters and identifies the emittingplatforms.

Intercepted audio band transmissions from unknown sources frequentlycontain modulations other than those associated with voice. Thesemodulations can be generated by propulsion systems, transmitters,heating and cooling equipment, or other systems associated with thetransmitting platform, and are unintentional. Such unintentionalmodulations can permit platform identification when properly detected,analyzed, and interpreted.

Various governmental and industrial organizations have signal collectionactivities which gather massive amounts of voice channel data. This datais or is not of interest, depending on the information contained in thevoice signal and any additional information that might be gleaned fromthe channel itself. Additional data from unintentional modulations canbe used to assist in culling uninteresting data and as a means ofproviding “external” cues to the identity or location of thetransmitting device. Such information is of value in search and rescuemissions. Exploring possible analysis techniques for exploitingunintentional modulations is a continuing activity of theseorganizations.

Some of these organizations are interested in the use of passive devicesto detect, select, and identify signal sources of interest. Since theactivities of the signal sources frequently are accompanied by radiotransmissions, it was deemed possible to identify the emitting platformby detecting and identifying unintentional modulations imposed on thetransmission. In addition, classifying emitters based on unintentionalmodulations can enhance the correlation of identification data fromother, usually known reliable, sources.

SUMMARY OF THE INVENTION

The principal object of this invention is to provide a feasible methodof using non-speech audio band information to obtain identificationinformation pertaining to the emitting platform.

A further object of the invention is to provide a method foridentification of the emitting platform by detecting and identifyingunintentional modulations imposed on the transmission.

Still a further object of the invention is to provide a method forclassifying emitters based on unintentional modulations to enhance thecorrelation of identification data from other sources.

Still another object of the invention is to provide a method to identifythe emitting platform based on its unique keyclick feature signature orunintentional modulations.

Another object of this invention is to provide an algorithm toautomatically detect, segment, and identify these unique keyingfeatures, the accuracy of the resulting algorithm performance beingbased on the “ground truth” associated with several emitting platforms.

In a first aspect of the invention, a system for emitter identificationof a transmitting platform using microphone keyclick analysis, comprisesa means to detect transmission activity on a radio channel in the audiofrequency range and a means to detect the boundaries of transmitterkeyclicks at the beginning and end of said transmission. It furtherincludes means to extract keying envelopes from said detected keyclickboundaries and means to analyze said keying envelopes to extractfeatures characteristic of said keyclicks. Finally, the system has meansto execute a decision function based on said extracted characteristicfeatures to identify the platform of said transmitter based on thefeatures of said keyclick.

In a second aspect of the invention, the system also includes means toprocess said transmission data using a fast fourier transform and meansto detect gaps in said processed transmission data and to extractspectral features consisting of normalized energy per unit frequency.The system further includes means to correlate identification of saidemitter platforms based on said extracted spectral features withidentification of said emitter platforms based on said extractedkeyclick features.

In a third aspect of the invention, a method of emitter identificationof a transmitting platform using microphone keyclick analysis, comprisesthe steps of: detecting transmission activity on a radio channel in theaudio frequency range; detecting the boundaries of transmitter keyclicksat the beginning and end of said transmission; extracting keyingenvelopes from said detected keyclick boundaries; analyzing said keyingenvelopes to extract features characteristic of said keyclicks; andexecuting a decision function based on said extracted characteristicfeatures to identify the platform of said transmitter based on thefeatures of said keyclick.

In a fourth aspect of the invention, the method of this inventionfurther comprises the steps of: processing said transmission data usinga fast fourier transform; detecting gaps in said processed transmissiondata; extracting spectral features consisting of normalized energy perunit frequency; extracting spectral features of each transmission fromsaid gaps; and correlating identification of said emitter platformsbased on said extracted spectral features with identification of saidemitter platforms based on said extracted keyclick features.

Other objects, features and advantages of the invention will be apparentto those skilled in the art from the following description of thepreferred embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic representation of a transmission detectionstate machine;

FIGS. 2a, 2 b show a keyclick from a Type 3 aircraft and its envelope,respectively;

FIGS. 3a, 3 b show a keyclick from a Type 2 aircraft and its envelope,respectively;

FIGS. 4a, 4 b show a two pop keyclick from a Type 1 aircraft and itsenvelope, respectively;

FIGS. 5a, 5 b show a three pop keyclick from a Type 1 aircraft and itsenvelope, respectively;

FIGS. 6a, 6 b show a ground controller keyclick and its envelope,respectively;

FIG. 7 is a plot illustrating the calculation of secondary keyclickfeatures, according to the preferred embodiment of this invention;

FIG. 8 is a scatter plot of the secondary features for keyclicks withtwo pops;

FIG. 9 is a scatter plot of the secondary features for keyclicks withthree pops;

FIG. 10 is a diagram of a keyclick decision tree, according to thepreferred embodiment of this invention;

FIG. 11 is a flow diagram of the steps of the keyclick recognitionalgorithm, according to the preferred embodiment of this invention;

FIG. 12 is a flow diagram of the steps of the filter bank algorithm,according to the preferred embodiment of the invention disclosed in thecross-referenced application;

FIG. 13 shows no-decision recognition test results for the filter bankrecognition algorithm using a decision tree;

FIG. 14 shows no-decision recognition test results for the filter bankrecognition algorithm using vector quantization;

FIG. 15 is a plot of a labeled transmission;

FIG. 16 is a plot of approximate keyclick boundaries;

FIG. 17 is a plot of precise keyclick boundaries;

FIG. 18 shows an envelope of the keyclick of FIG. 17;

FIG. 19 is a confusion matrix of recognition results using the preferredembodiment of the keyclick algorithm; and

FIG. 20 is a flow chart illustrating an embodiment of the keyclickalgorithm of this invention and an embodiment of the filter bankalgorithm of the cross-referenced invention operating in parallel on thesame data.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

This invention pertains to algorithms within a system for emitteridentification from radio signals (EMIRS) The system comprises acollection of digital signal processing algorithms which automaticallydetect, segment, and identify the on-off keying signatures ofpush-to-talk audio frequency transmitters. In sequence, the algorithmsdetect channel activity and the presence of an on-off keying event;segment the event from the accompanying speech signal; analyze the eventfor key features; and based on these features, perform a directeddecision search through prestored, signal source signatures to determinethe identify of the push-to-talk emitter. EMIRS was implemented on a SUNworkstation and a VAX workstation using Unix and the C language.

By definition, audio band data are contained in a restricted bandwidth,usually 300 to 3000 Hz in width. The bandwidth can vary according to therange of transmission conditions and transmitters. Push-to-talkmicrophone and radio transmitter combinations frequently have uniquekeying features, and generic classes of platforms usually are equippedwith a given class of transmitters. It should be possible, therefore, toidentify the emitting platform by detecting and identifyingunintentional modulations or its unique feature signature imposed on thetransmission. In addition, classifying emitters based on unintentionalmodulations can enhance the correlation of identification data fromwilling sources.

Based on this logic, the algorithms of this invention were developed toautomatically detect, segment, and identify these unique keyingfeatures. The accuracy of the resulting algorithm performance was basedon the “ground truth” associated with several emitting platforms. Groundtruth, as used here, means that the precise identification of theplatform was known a priori from other sources. The development effortrequired finding appropriate databases to use in the investigation. Anappropriate database requires that its data be realistic for theproblem, and contain “ground truth” information. Two sample databaseswere located for development of these algorithms, each involvingtransmissions to or from aircraft. These will be designated DB ONE andDB TWO. The aircraft will be designated Type 1 through Type 6. Ahelicopter and ground control are also included in one or both of thedatabases.

The development also required finding appropriate features to extractthe data bases for identification, and finding appropriate algorithms tocarry out the identification. Feature selection involved examination ofrelevant data in both the time and frequency domains, and the expertisethat could be offered by an operational linguist skilled at listening tosuch data. Since most transmissions were dominated by speech data,appropriate gaps had to be located, or techniques found to suppress thespeech data. For operational data, the knowledge of a human expert wasused.

Several major approaches to identification algorithms were used in theseinvestigations. The first approach investigated distinctive features inthe time domain. The most distinctive features were the keyclicks whichpreceded and followed each transmission. Significant recognitionperformance resulted from this approach, using a keyclick algorithm,which is the subject of this application.

DB ONE includes transmissions involving three types of aircraft andground controllers, recorded during pilot training exercises. Complete“ground truth” information about aircraft types, tail numbers, and pilotidentities is included with the database. The entire database consistsof thousands of transmissions from various training exercises. A subsetof the higher quality transmissions was used for developing thealgorithms of this invention.

KEYCLICK INVESTIGATIONS

In DB ONE it was found that the keyclicks of radio transmissions fromaircraft in the audio frequency band, particularly the offclicks,contain useful information. It was found that the envelopes of suchclicks consisted of a series of “pops”, corresponding to peaks. Thenumber of pops in a keyclick and certain related features were used,along with a decision tree or vector quantization approach toidentification. Performance at an 80% level for distinguishing among theaircraft types involved was obtained.

The keyclick investigations focused on finding discriminating featuresin the off-keyclicks of a platform's radio transmissions. The list ofprocesses that comprise the Keyclick Identification algorithm are:

A. Transmission Detection.

B. Click Detection.

C. Feature Extraction and identification.

Even though transmission detection and keyclick detection are notinvolved in actual identification, they are processes whose performancecannot be taken for granted. For that reason, considerable effort wasexpended to develop effective transmission and keyclick recognizers inaddition to identifying keyclick features and decision methods.

TRANSMISSION DETECTION

Transmission detection is accomplished through energy thresholding over12 millisecond frames of data to determine the approximate boundaries ofa transmission. The transmission detector is implemented as a statemachine 10, illustrated in FIG. 1, with four states, 12, 14, 16 and 18.Two of the states corresponded to being within a transmission 16 andoutside of a transmission 12. Two additional states 14, 18 are used forthe transitions between the transmit-on and transmit-off states. Theenergy threshold which determines the state is related to an adaptivenoise floor that is updated at least once every second. The noise flooris defined as the minimum frame energy during the past one second ofdata. If the noise floor remains unchanged for more than one second, itis reset to the minimum value over the past second, thus allowing thenoise floor to adapt to changing levels of background noise. The noisefloor adaptation is disabled while not in the transmit-off state. Theenergy threshold was set experimentally, as a constant multiplied by thenoise floor.

The state machine transitions are defined as follows: While in thetransmit-off state 12, if a frame's energy was greater than the energythreshold, the state changed to the transition state 14 fromtransmit-off to transmit-on. To reach the transmit-on state 16, threeconsecutive frames (36 milliseconds) of data must have energies abovethe defined threshold; otherwise the state machine 10 returns to thetransmit-off state 12. While in the transmit-on state 16, a frame'senergy below the threshold would cause the state machine to change tothe transition state 18 from transmit-on to transmit-off state. Tocontinue to the transmit-off state 12, twelve (12) consecutive frames(144 milliseconds) of data must have energy values below the energythreshold; otherwise the state machine 10 returns to the transmit-onstate 18.

FIGS. 2a, 2 b through 6 a, 6 b show five off-keyclicks and theirenvelopes (as defined below), representative of the emitting platformsin DB ONE. Note that the envelopes each contain a sequence of “pops”,each pop corresponding to a peak.

An off-keyclick detector uses the approximate end-of-transmissionboundary produced by the transmission detector as a rough estimate ofthe keyclick location. The 2048 points of data (205 milliseconds)surrounding the end-of-transmission are cross-correlated with a poptemplate over 1024 lags in the time domain. The template is a sinusoidwhose amplitude is damped and whose frequency decays with time over a128-sample period.

Since keyclicks are composed of a series of pops, analyzing thecross-correlation output for peaks reveals the locations in theend-of-transmission buffer that most resemble a “pop”. The first “pop”detected and the last “pop” detected in the cross-correlation outputcorrespond to the precise boundaries of the keyclick in theend-of-transmission buffer.

FEATURE EXTRACTION AND IDENTIFICATION

The envelope of the keyclick was determined using a two-stage leakyintegrator of the form

y[i]=w*y[i−1]+(1−w)*x[i]

where x[ ] is the input stream, y[ ] is the output stream, and w is theweight factor that is specified as an input parameter. Experimentationdetermined that two iterations of the integrator with w=0.9 followed byw=0.8 produce accurate envelopes.

The features used for identification are statistics derived from theenvelope of the keyclick detected in the previous step. Thesestatistics, illustrated in FIG. 7, are:

A. Number of pops (peaks), labeled P1, P2, P3, contained in thekeyclick;

B. The average ratio of each pop's amplitude to the first pop'samplitude:${{{Amplitude}\quad {Ratio}} = \frac{{{P2}/{P1}} + {{P3}/{P1}}}{2}},{and}$

C. The average ratio of each pop's peak amplitude to its followingvalley, labeled V1, V2:${{Park}\quad {to}\quad {Volley}\quad {Ratio}} = {\frac{{{P1}/{V1}} + {{P2}/{V2}}}{2}.}$

Identification of the keyclick was accomplished by applying the featuresextracted to one of two decision making routines explored. Scatter plotsof these features are shown in FIGS. 8 and 9.

The first decision method is a simple decision tree routine. FIG. 10 isan illustration of the keyclick decision tree 20, as used in thepreferred embodiment of this invention. The decision tree 20 wasinitially determined by observing a small set of transmissions,obtaining scatter plots of the features extracted and determiningthresholds for each rule. The decision rules were slightly modified asmore data was added and eventually frozen before running final tests ona completely new set of data. A set of rules was tested for each of thefeatures extracted. Decision tree 20 has two levels, 22, 26. At thestart 21, the first level 22 decision is made on the basis of the numberof pops. After a decision on level 22, the next level 26 decision ismade on the basis of secondary features. For example, if the number ofpops found in a keyclick was greater than three, the keyclick wasidentified as belonging to a ground station 25. If there were three popsin the keyclick, it belonged to a Type 3 or a Type 1 aircraft, 24. Iftwo pops were found, it was either a Type 2 or a Type 1 aircraft, 23.Secondary testing was then applied based on the location of the keyclickin the plane (cf. FIGS. 8, 9) defined by the remaining two features,peak-to-valley-ratio (PVR) and average-amplitude-ratio (AAR). In thecase of a keyclick with three pops, if its location on the PVR-AAR planeis above the line:

PVR=M1×AAR+B1 (M1=0.05, B1=0.80),

where M1, B1 are the slope and intercept respectively, then it wasidentified as a Type 3, 29. If below the line, it is identified as aType 1, 28. Similarly, with a keyclick consisting of two pops, if itslocation in the PVR-AAR plane is above the line:

PVR=M2×AAR+B2 (M2=0.227, B2=0.236),

where M2, B2 are the slope and intercept respectively, it is identifiedas a Type 1, 28. Otherwise, it is identified as a Type 2, 27. See FIG.10.

FIG. 11 is a block diagram summarizing the algorithmic steps 40 ofemitter identification from radio signals (EMIRS), using the the thekeyclick algorithm with decision tree, according to the preferredembodiment of this invention. This embodiment of EMIRS processing can besummarized in the following algorithmic steps.

1. Detect channel activity 41;

2. Detect boundaries for transmitter keying 42;

3. Extract keying envelope 43;

4. Analyze for keying envelope features 44:

a. number of envelope peaks (pops);

b. determine amplitude ratios among peaks;

c. determine peak-to-valley ratios of envelope peaks; and

5. Execute the decision function based on the features extracted 45.

A second decision method investigated as an alternative to the decisiontree of FIGS. 10, 11, was a Vector Quantization (VQ) approach. The samefeature set was used as in the previous algorithm of FIG. 11. Thetraining set used to create codebooks was the same used for initialrecognition of the decision tree method. Codebooks with one, two, andfour clusters were created for each platform type. During recognition,an input feature vector was scored with each codebook, returning theminimum distortion calculated. A decision was then made based on thedistortion values returned for each platform. If no one distortion wassignificantly better then the rest, a no-decision was returned.

Finally, a report generation program was used to present theidentification determination for each transmission and the cumulativeresults in a confusion matrix. (See FIG. 15.)

DB TWO KEYWORD DATABASE INVESTIGATIONS

The second approach to emitter identification from radio signalsexamined spectral features of transmission data. These included FFTs,bandpass filter energies (derived from FFTs), and spectral coefficients.Various means of representation, including spectrograms and threedimensional wavefall plots were utilized. It was clear from listening toDB TWO that there was relevant spectral information in that database.Therefore the spectral approach was adopted, using normalized energiesin seven bandpass filters. Significant recognition performance resultedfrom this approach, using a filterbank algorithm, which is the subjectof the cross-referenced application. Performance of 80% or better atdistinguishing the emitter platform types involved (as identified by ahuman expert) was obtained.

DB TWO is a keyword database assembled for keyword spotting algorithmdevelopment. It consists of transmissions of operational data withspeech in a foreign language. “Ground truth” information is notincluded. A former communications operator served as the expert for thisdata to supply ground truth information for the transmissions. Bylistening to transmissions, and relying on his considerable experienceas an operational linguist, he recognized sounds such as whines and humswhich identified broad aircraft type. The expert listened to the data,and identified the following types of emitting platforms in thesetransmissions: ground transmissions, helicopter transmissions, oraircraft transmissions from one of three types of aircraft, hereindesignated as Type 4, Type 5 or Type 6. These identifications served as“ground truth” to test the algorithms on this data.

Examination of time domain signals in DB TWO showed that thesetransmissions had keyclicks, but that the types of transmissions werenot as readily distinguished by keyclicks as those examined in the datafrom DB ONE. In particular, all these keyclicks appeared to consist ofjust one pop. It was clear from preliminary listening tests that therewas useful spectral information in the transmissions for identifying thevarious aircraft platforms, particularly in the speech gaps.

A preliminary investigation was performed using twenty transmissions ofthe DB TWO Keyword Database. The transmissions are described by Table 1.(For convenience, the tables appear at the end of this specification innumerical order.) Each transmission contains at least one gap asdetermined by a gap detection algorithm. A simple set of spectralfeatures was chosen for gap detection. This set consisted of normalizedenergy per unit frequency in the following frequency bands:

50 to 500 Hz,

500 to 1000 Hz,

1000 to 1500 Hz,

1500 to 2000 Hz,

2000 to 2500 Hz,

2500 to 3000 Hz, and

3000 to 4000 Hz.

The region from 0 to 50 Hz was neglected to minimize detrimental effectsdue to DC offsets.

ALGORITHM

FIG. 12 is a block diagram of the preferred embodiment of a Filter Bankalgorithm 60, which is claimed in the cross-referenced application, andwhich was developed to identify the emitting platforms exemplified inthe data of DB-TWO. The intercepted transmission data is fed to a fastfourier transform algorithm 61 for initial processing. A 256-point FFTwas used with a frame rate of sixty-four (64) samples (8 μsec.). The gapdetector 62 makes a decision for each frame based on the peak-to-averageenergy value of the frame, thus determining the boundaries of the gapswithin transmissions. Only frames labeled “gap”, that is gaps in thespeech signal, were processed further for feature extraction 63. Havingextracted the features of each transmission, that is, the normalizedenergies in seven bandpass filters as indicated above, the feature fileis processed for decision making 64. An output decision was made foreach gap frame using a decision tree to determine an aircraft type fromthe frame's feature vector. The final transmission decision is the typemost frequently chosen over all the gap frames of the transmission.

Table 2 specifies recognition results where recognition was performed oneach hand-labeled gap. Thus transmissions with several gaps had severalrecognition trials. If different gaps of a transmission had differentrecognition outcomes, the transmission is recognized as uncertain (U).

Table 3 specifies recognition results where recognition is performed oneach transmission. Here gaps were determined automatically using apeak-to-average energy statistic.

FINAL TESTING

KEYCLICK INVESTIGATIONS

Final testing of the keyclick recognition and filter bank algorithms wasperformed on a set of data distinct from what had been used duringalgorithm development. One hundred new transmissions were selected,evenly distributed among four emitting platforms to be recognized. Thisfirst test performed was on the Decision Tree algorithm using automatictransmission and keyclick labeling capabilities. An improvedtransmission detector was used in this test. The confusion matrix inTable 4 shows the results with forced decisions (0% no-decision rate). Asecond test was run using hand-labeled off-keyclicks from the final testset. The forced decision results are illustrated in Table 5.

Tests were run that varied the no-decision rates for the algorithmversions described above. FIG. 13 depicts the relative recognitionlevels for the algorithms (including an early version of thetransmission detector) and the effects of changing decision rates. Itwas apparent that the no-decision capabilities had no significant effecton the Decision Tree algorithms.

The next set of tests involved the Vector Quantization (VQ) keyclickrecognition module. In each case, the hand-labeled set of final testkeyclicks was used (for comparison with the hand-labeled Decision Treetests). The average click duration was about 500 samples, or 64milliseconds. In these tests, training was performed on the set of dataused for initial recognition in the Decision Tree investigations. Thisincluded 11 ground transmissions, 26 Type 3 aircraft transmissions 16Type 1 aircraft transmissions, and 36 Type 2 aircraft transmissions.

The three VQ experiments used codebooks containing one cluster centerfor each platform, two cluster centers per platform, and four clustercenters per platform. The forced decision confusion matrices for thetests are shown in Tables 6, 7, and 8. The tables also show how manytraining instances were included in each cluster.

The results are surprising in that the test using a VQ codebook of sizeone out-performed the other two tests. The only difference between thefirst VQ test results and the second was the drop in Type 1 aircraftrecognition. The final VQ test using a size four codebook gainedslightly in Type 1 aircraft recognition, but not enough to raise totalperformance to the levels of the size one codebook test.

While the VQ recognition using multiple cluster codebooks wereout-performed by the single cluster codebook, the effects of varieddecision rates were more significant with the multiple cluster codebooksthan the single cluster codebook. FIG. 14 shows the relative recognitionresults for no-decision rates of zero percent, five percent, and tenpercent for each of the VQ tests.

Despite the slightly better performance of the Decision Tree algorithmto the VQ algorithm, it is believed that the VQ algorithm shows greaterpromise. It is believed that VQ performance can be improved with moreexperimentation while the Decision Tree results represent optimalperformance. The lack of an effective no-decision capability for theDecision Tree is another shortfall in its comparison with VQ methods.

INTERPRETATION

The keyclick results on the final test data indicate that it is aneffective algorithm for distinguishing aircraft types from transmissionsof the type included in the database. The errors that occur are of threetypes: inaccurate transmission detection, inaccurate keyclick boundarydetermination, and confusions of Type 1 aircraft with Type 3 aircraft orType 2 aircraft due to keyclicks where the number of pops does notdetermine the result.

Improved transmission detection eliminated most errors of the firsttype. It remains the case that performance improves, on the order of tenpercent, when keyclick boundaries are hand-labeled. Therefore, furtherwork must be done to improve the automatic keyclick boundary detectionprocedure.

The secondary features correctly resolve most potential confusions ofType 1 aircraft with Type 3 aircraft or Type 2 aircraft with the samenumber (three or two, respectively) of pops. Correcting the errors thatdo occur may be a more difficult problem.

CONCLUSIONS

Two algorithms for identifying transmission sources have been disclosed.One uses off-click signatures from audio transmissions. The second,developed using DB-TWO Keyword database, uses a digital filterbank. Thissection summarizes these algorithms.

KEYCLICK RECOGNITION

In the keyclick recognition algorithm, as shown in FIG. 11, thefollowing processes operate on each transmission in the data file:

A. Transmission Detection.

B. Click Detection.

C. Feature Extraction and identification.

Transmission Detection

Transmission detection is accomplished through energy thresholding over16 millisecond frames data to determine the approximate boundaries of atransmission. A labeled transmission, such as that in FIG. 15, isdisplayed as output from this step (41 in FIG. 11).

Click Detection

The off-keyclick detector 42 uses the approximate end-of-transmissionboundary produced by the transmission detector as a rough estimate ofthe keyclick location. The end-of-transmission time and the surrounding2048 points of data (0.25 seconds) are cross-correlated in the timedomain with a pop template. Since keyclicks are composed of a series ofpops, thresholding on the resulting cross correlation results in precisekeyclick boundaries. The keyclick envelope 43 is determined using atwo-stage leaky integrator. The envelope is displayed as in FIG. 18.Approximate and more precise keyclick boundaries, as suggested by FIGS.16 and 17, respectively, are displayed as output from this step 43.

Feature Extraction and Identification

The features used for identification 44 are statistics derived from theenvelope of the keyclick detected in the previous step 43.Identification of the keyclick is accomplished by applying the featuresextracted to one of two decision making routines explored 45.

A report generation program is used to present the identificationdetermination for each transmission and the cumulative results in aconfusion matrix such as that shown in FIG. 19.

DB-TWO KEYWORD DATABASE INVESTIGATIONS

The filter bank algorithm, shown in FIG. 12, developed for the KeywordDatabase operates on a set of preprocessed feature files representingthe majority of the transmissions labeled so far. This algorithm is, ineffect, an additional means of feature extraction for cases which cannotbe decided using the keyclick algorithm. For this algorithm, thefeatures extracted are the normalized energies in seven bandpassfilters, which, on the basis of experimentation, are distinctivelyrepresentative of different types of emitter platforms.

PARALLEL OPERATIONS

FIG. 20 is a flow chart showing parallel operation 50 of embodiments ofboth algorithms. The same signal inputs 51 are processed by the keyclick algorithm and the filter bank algorithm simultaneously. In thekeyclick algorithm, the keyclick boundaries are detected 51, thekeyclick envelope is determined 53, the features, such as number orpeaks, AAR and PVR, are extracted 54. Then the results of the processingthus far are sent to a decision module 58. Simultaneously, the inputsignal is processed by a fast fourier transform 55, the gaps aredetected 56, and the features, such as normalized energies in sevenbandpass filters, are extracted 57. The results of this processing arealso sent to the decision module 58. The decision module can use eithera decision tree or a vector quantization routine to make anidentification decision based on the results of both algorithms.

The decision function used can be either a decision tree search or acodebook search, depending on the nature of the extracted features. Bothfunction types were executed and work equally well. Similarly, EMIRS hastwo feature extraction functions which can be used separately or intandem to identify the emitting platform.

EMIRS was implemented on a SUN workstation and a VAX workstation usingUnix and the C language.

TABLE 1 RADC Keyword Database Transmissions. Number of PlatformTransmissions Total Gaps Individual Tails H 4 7 2 Type 4 7 11   >=4 Type5 4 6 3 Type 6 5 7 3 Total 20  31  >=12

TABLE 2 Recognition Results Using Hand-labeled Gaps. RECOGNIZED AS HType 4 Type 5 Type 6 U TOTALS A H 3 0 0 0 1 3/4 75% C Type 4 0 5 1 0 15/7 71% T Type 5 0 0 4 0 0 4/4 100%  U Type 6 0 0 0 5 0 5/5 100%  A17/20 85% L

TABLE 3 Recognition on Transmission Using Automatically Detected Gaps.RECOGNIZED AS H Type 4 Type 5 Type 6 TOTALS A H 4 0 0 0 4/4 100%  C Type4 2 4 1 0 4/7 57% T Type 5 0 0 3 1 3/4 75% U Type 6 0 0 0 5 5/5 100%  A16/20 80% L

TABLE 4 Results for Improved Automatic Keyclick Detection with DecisionTree Ground Type 3 Type 1 Type 2 SCORE GROUND 20  3 0 2 20/25 80.00%Type 3 3 20  1 2 20/26 76.92% Type 1 7 0 18  0 18/25 72.00% Type 2 1 0 518  18/24 75.00% NO-DECISIONS:  0/100  0.00% TOTAL:  76/100 76.00%

TABLE 5 Results for Hand Labeled Keyclicks with Decision Tree GroundType 3 Type 1 Type 2 SCORE GROUND 22  0 1 2 22/25 88.00% Type 3 1 22  12 22/26 84.62% Type 1 4 0 21  0 21/25 84.00% Type 2 1 0 3 20  20/2483.33% NO-DECISIONS:  0/100  0.00% TOTAL:  85/100 85.00%

TABLE 6 Results for Hand Labeled Keyclicks with Vector QuantizationCodebook Size One Training Cluster Ground Type 3 Type 1 Type 2 SCORESizes GROUND 19  0 5 1 19/25 76.00% 11 Type 3 0 22  3 1 22/26 84.62% 26Type 1 0 0 25  0 25/25 100.00%  16 Type 2 0 1 7 16  16/24 66.67% 36NO-DECISIONS:  0/100  0.00% TOTAL:  82/100 82.00%

TABLE 7 Results for Hand Labeled Keyclicks with Vector QuantizationCodebook Size One Training Cluster Ground Type 3 Type 1 Type 2 SCORESizes GROUND 19  0 5 1 19/25 76.00%  5, 6 Type 3 1 22  1 2 22/26 84.62%17, 9 Type 1 0 0 17  8 17/25 68.00%  7, 9 Type 2 0 0 8 16  16/24 66.67% 21, 15 NO-DECISIONS:  0/100  0.00% TOTAL: 74/100 74.00%

TABLE 8 Results for Hand Labeled Keyclicks with Vector QuantizationCodebook Size One Training Cluster Ground Type 3 Type 1 Type 2 SCORESizes GROUND 18  1 4 2 18/25 72.00% 2, 3, 3, 3 Type 3 2 21  0 3 21/2680.77% 6, 10, 5, 5  Type 1 0 0 21  4 21/25 84.00% 3, 4, 5, 4 Type 2 0 08 16  16/24 66.67% 6, 14, 6, 10 NO-DECISIONS:  0/100  0.00% TOTAL: 76/100 76.00%

We claim:
 1. A system for emitter identification of a transmittingplatform using microphone keyclick analysis, comprising: means to detecttransmission activity on a radio channel in the audio frequency range;means to detect the boundaries of transmitter keyclicks at the beginningand end of said transmission; means to extract keying envelopes fromsaid detected keyclick boundaries; means to analyze said keyingenvelopes to extract features characteristic of said keyclicks; means toexecute a decision function based on said extracted characteristicfeatures to identify the platform of said transmitter based on thefeatures of said keyclick.
 2. The system of claim 1, wherein said meansto detect transmission activity comprises: means to select an adaptivenoise floor; means to update said noise floor periodically; means todetermine an energy threshold of a transmission relative to saidadaptive noise floor; means to determine the transitions from transmiton to transmit off relative to said adaptive noise floor, therebydetermining end of transmission boundaries; and means to store said endof transmission boundaries.
 3. The system of claim 2, wherein said meansto detect the boundaries of a transmitter keyclick further comprises:means to analyze said approximate end of transmission boundary todetermine energy peaks; means to cross-correlate said peaks with atemplate of known emitters, thereby detecting the precise boundaries ofa keyclick.
 4. The system of claim 3, wherein said means to extract akeying envelope further comprises: means to develop an envelope aroundeach peak using a leaky integration of the form:y[i]=w*y[i−1]+(1−w)*x[i] where x[ ] is the input stream, y[ ] is theoutput stream, and w is the weight factor that is specified as an inputparameter.
 5. The system of claim 4, wherein said means to extractfeatures characteristic of keyclicks, comprises: means to determine thenumber of peaks in each of said envelopes; means to calculate theaverage ratio of each peak to the first peak in each of said envelopes;means to calculate the average ratio of each peak to its followingvalley in each of said envelopes; thereby extracting statistical dataconstituting said features.
 6. The system of claim 5, wherein said meansto execute a decision function further comprises: means to establish adecision tree based on scatter plots of past known identities; means toestablish a set of rules based on past analyses, and to determinethresholds for said rules; and means to apply currently extractedfeatures to a data base constructed with said rules and thresholds. 7.The system of claim 5, wherein said means to identify an emittingplatform further comprises: means to utilize a vector quantizationalgorithm to determine minimum distortion of a current vector comparedto past vectors.
 8. The system of claim 7, wherein said vectorquantization algorithm comprises: at least one codebook having at leastone cluster developed on the basis of training data; means to score saidextracted features with each codebook; means to calculate the minimumdistortion of said extracted features; and means to identify a platformbased on said scores.
 9. The system of claim 1, further comprising:means to process said transmission data using a fast fourier transform;means to detect gaps in said processed transmission data; means toextract spectral features of each transmission from said gaps,consisting of normalized energy per unit frequency; and means tocorrelate identification of said emitter platforms based on saidextracted spectral features with identification of said emitterplatforms based on said extracted keyclick features.
 10. A method ofemitter identification of a transmitting platform using microphonekeyclick analysis, comprising the steps of: detecting transmissionactivity on a radio channel in the audio frequency range; detecting theboundaries of transmitter keyclicks at the beginning and end of saidtransmission; extracting keying envelopes from said detected keyclickboundaries; analyzing said keying envelopes to extract featurescharacteristic of said keyclicks; executing a decision function based onsaid extracted characteristic features to identify the platform of saidtransmitter based on the features of said keyclick.
 11. The method ofclaim 10, wherein said step of detecting transmission activity furthercomprises the steps of: selecting an adaptive noise floor; updating saidnoise floor periodically; determining an energy threshold of atransmission relative to said adaptive noise floor; determining thetransitions from transmit on to transmit off relative to said adaptivenoise floor, thereby determining end of transmission boundaries; andstoring said end of transmission boundaries.
 12. The method of claim 11,wherein said step of detecting the boundaries of a transmitter keyclickfurther comprises the steps of: analyzing said approximate end oftransmission boundary to determine energy peaks; and cross-correlatingsaid peaks with a template of known emitters, thereby detecting theprecise boundaries of a keyclick.
 13. The method of claim 12, whereinsaid step of extracting a keying envelope further comprises the step of:developing an envelope around each peak using a leaky integration of theform: y[i]=w*y[i−1]+(1−w)*x[i] where x[ ] is the input stream, y[ ] isthe output stream, and w is the weight factor that is specified as aninput parameter.
 14. The method of claim 13, wherein said step ofextracting features characteristic of keyclicks, further comprises thesteps of: determining the number of peaks in each of said envelopes;calculating the average ratio of each peak to the first peak in each ofsaid envelopes; and calculating the average ratio of each peak to itsfollowing valley in each of said envelopes; thereby extractingstatistical data constituting said features.
 15. The method of claim 14,wherein said step of executing a decision function further comprises thesteps of: establishing a decision tree based on scatter plots of pastknown identities; establishing a set of rules based on past analyses,and determining thresholds for said rules; and applying currentlyextracted features to a data base constructed with said rules andthresholds.
 16. The method of claim 15, wherein said step of identifyingan emitting platform further comprises the step of: utilizing a vectorquantization algorithm to determine minimum distortion of a currentvector compared to past vectors.
 17. The method of claim 16, whereinsaid step of utilizing a vector quantization algorithm further comprisesthe steps of: utilizing at least one codebook having at least onecluster developed on the basis of training data; scoring said extractedfeatures with each codebook; calculating the minimum distortion of saidextracted features; and identifying an emitting platform based on saidscores.
 18. The method of claim 10, further comprising the steps of:processing said transmission data using a fast fourier transform;detecting gaps in said processed transmission data; extracting spectralfeatures of each transmission from said gaps, consisting of normalizedenergy per unit frequency; and correlating identification of saidemitter platforms based on said extracted spectral features withidentification of said emitter platforms based on said extractedkeyclick features.